pin to newer version of CLIP that returns encoded text and images, get some helper functions ready for XCLIP

dalle2_pytorch/dalle2_pytorch.py

@@ -3,6 +3,48 @@ import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange
 
+# use x-clip
+
+from x_clip import CLIP
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+# for controlling freezing of CLIP
+
+def set_module_requires_grad_(module, requires_grad):
+    for param in module.parameters():
+        param.requires_grad = requires_grad
+
+def freeze_all_layers_(module):
+    set_module_requires_grad_(module, False)
+
+def unfreeze_all_layers_(module):
+    set_module_requires_grad_(module, True)
+
+# diffusion prior
+
+class DiffusionPrior(nn.Module):
+    def __init__(self):
+        super().__init__()
+    def forward(self, x):
+        return x
+
+# decoder
+
+class Decoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+    def forward(self, x):
+        return x
+
+# main class
+
 class DALLE2(nn.Module):
     def __init__(self):
         super().__init__()

setup.py

@@ -17,7 +17,7 @@ setup(
   install_requires=[
     'einops>=0.4',
     'torch>=1.6',
-    'x-clip'
+    'x-clip>=0.4.1'
   ],
   classifiers=[
     'Development Status :: 4 - Beta',